我正在嘗試從兩個不同的 HTML 源中抓取 HTML 表格。兩者非常相似，每個表都包含相同的數據，但它們的結構可能不同，具有不同的列名稱等。對于一個源，所有數據可能包含在一個表中，而另一個源可能將數據分解為兩個單獨的桌子。作為一個例子，我們可以看看 AAPL 和 MMM 股票的內部持有者。屏幕截圖在這里 - https://i.stack.imgur.com/dt6Pa.jpg可以說，最終目標是提取內部人士持有的股份總數——一個單一數字。每個表格的結構可能不同，但應該相似的是“受益”或“股票”等關鍵詞。任何幫助將不勝感激。在上一篇文章中，我能夠提取一些數據。但如果結構不同，則不能循環或重復。根據特定列標題提取 HTML 表 - Pythondf = pd.read_html("https://www.sec.gov/Archives/edgar/data/66740/000120677420000907/mmm3661701-def14a.htm", attrs={'style': 'border-collapse: collapse; width: 100%; font: 9pt Arial, Helvetica, Sans-Serif'}, match="Name/address")df = df[0]df = df.dropna(axis = 'columns')還嘗試過BSurl = 'https://www.sec.gov/Archives/edgar/data/66740/000120677420000907/mmm3661701-def14a.htm'r = requests.get(url) soup = BeautifulSoup(r.text, 'html.parser')tables = soup.find_all('table')rows = tables.find_all('tr')
1 回答
素胚勾勒不出你
TA貢獻1827條經驗 獲得超9個贊
這確實很復雜，但我們開始吧:)。
import requests
from bs4 import BeautifulSoup
import re
import pandas as pd
urls = ['https://www.sec.gov/Archives/edgar/data/320193/000119312520001450/d799303ddef14a.htm',
'https://www.sec.gov/Archives/edgar/data/66740/000120677420000907/mmm3661701-def14a.htm']
def main(urls):
with requests.Session() as req:
for url in urls:
r = req.get(url)
soup = BeautifulSoup(r.content, 'html.parser')
for item in soup.findAll("a", text=re.compile("^Security")):
item = item.get("href")[1:]
catch = soup.find("a", {'name': item}).find_next("table")
df = pd.read_html(str(catch))
print(df)
df[0].to_csv(f"{item}.csv", index=False, header=None)
main(urls)
輸出：
[ 0 ... 8
0 NaN ... NaN
1 NaN ... NaN
2 Name of Beneficial Owner ... NaN
3 NaN ... NaN
4 The Vanguard Group ... %
5 NaN ... NaN
6 BlackRock, Inc. ... %
7 NaN ... NaN
8 Berkshire Hathaway Inc. / Warren E. Buffett ... %
9 NaN ... NaN
10 Kate Adams ... NaN
11 NaN ... NaN
12 Angela Ahrendts ... NaN
13 NaN ... NaN
14 James Bell ... NaN
15 NaN ... NaN
16 Tim Cook ... NaN
17 NaN ... NaN
18 Al Gore ... NaN
19 NaN ... NaN
20 Andrea Jung ... NaN
21 NaN ... NaN
22 Art Levinson ... NaN
23 NaN ... NaN
24 Luca Maestri ... NaN
25 NaN ... NaN
26 Deirdre O’Brien ... NaN
27 NaN ... NaN
28 Ron Sugar ... NaN
29 NaN ... NaN
30 Sue Wagner ... NaN
31 NaN ... NaN
32 Jeff Williams ... NaN
33 NaN ... NaN
34 All current executive officers and directors a... ... NaN
[35 rows x 9 columns]]
[ 0 1 ... 18 19
0 Name and principal position NaN ... Percent of Class NaN
1 Thomas “Tony” K. Brown, Director NaN ... (5) NaN
2 Pamela J. Craig, Director NaN ... (5) NaN
3 David B. Dillon, Director NaN ... (5) NaN
4 Michael L. Eskew, Director NaN ... (5) NaN
5 Herbert L. Henkel, Director NaN ... (5) NaN
6 Amy E. Hood, Director NaN ... (5) NaN
7 Muhtar Kent, Director NaN ... (5) NaN
8 Edward M. Liddy, Director NaN ... (5) NaN
9 Dambisa F. Moyo, Director NaN ... (5) NaN
10 Gregory R. Page, Director NaN ... (5) NaN
11 Patricia A. Woertz, Director NaN ... (5) NaN
12 Michael F. Roman, Chairman of the Board, Presi... NaN ... (5) NaN
13 Inge G. Thulin, Former Executive Chairman of t... NaN ... (5) NaN
14 Nicholas C. Gangestad, Senior Vice President a... NaN ... (5) NaN
15 Ashish K. Khandpur, Executive Vice President, ... NaN ... (5) NaN
16 Julie L. Bushman, Executive Vice President, In... NaN ... (5) NaN
17 Joaquin Delgado, Former Executive Vice Preside... NaN ... (5) NaN
18 Michael G. Vale, Executive Vice President, Saf... NaN ... (5) NaN
19 All Directors and Executive Officers as a Grou... NaN ... (5) NaN
[20 rows x 20 columns]]
[ 0 1 ... 6 7
0 Name/address NaN ... Percent of Class NaN
1 The Vanguard Group(1) 100 Vanguard Blvd. Malve... NaN ... 8.78 NaN
2 State Street Corporation(2) State Street Finan... NaN ... 7.36 NaN
3 BlackRock, Inc.(3) 55 East 52nd Street New Yor... NaN ... 7.30 NaN
[4 rows x 8 columns]]
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